Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-3197-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
12 Jul 2022
Data description paper |
| 12 Jul 2022
| 12 Jul 2022
Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China
Junting Zhong
State Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Xiaoye Zhang
CORRESPONDING AUTHOR
State Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Center for Excellence in Regional Atmospheric Environment, IUE,
Chinese Academy of Sciences, Xiamen 361021, China
Ke Gui
State Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Jie Liao
National Meteorological Information Center, Beijing 100081, China
Ye Fei
National Meteorological Information Center, Beijing 100081, China
Lipeng Jiang
Earth System Numerical Prediction Center, Beijing 100081, China
Lifeng Guo
State Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Liangke Liu
Department of Earth System Science, Tsinghua University, Beijing
100084, China
Huizheng Che
State Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Yaqiang Wang
State Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Deying Wang
State Key Laboratory of Severe Weather & Key Laboratory of
Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences,
Beijing 100081, China
Zijiang Zhou
National Meteorological Information Center, Beijing 100081, China
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Ke Gui, Huizheng Che, Yu Zheng, Hujia Zhao, Wenrui Yao, Lei Li, Lei Zhang, Hong Wang, Yaqiang Wang, and Xiaoye Zhang
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A unique episode with extreme biomass burning (BB) impact, with daily concentration of levoglucosan as high as 4.37 µg m-3, was captured at an area upwind of Beijing. How this extreme BB pollution event was generated and what were the chemical properties of PM2.5 under this kind severe BB pollution level in the real atmospheric environment were both presented in this measurement report. Moreover, the variation of the ratios of BB tracers during different BB pollution periods was also exhibited.
Sunling Gong, Hongli Liu, Bihui Zhang, Jianjun He, Hengde Zhang, Yaqiang Wang, Shuxiao Wang, Lei Zhang, and Jie Wang
Atmos. Chem. Phys., 21, 2999–3013, https://doi.org/10.5194/acp-21-2999-2021, https://doi.org/10.5194/acp-21-2999-2021, 2021
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Surface concentrations of PM2.5 in China have had a declining trend since 2013 across the country. This research found that the control measures of emission reduction are the dominant factors in the PM2.5 declining trends in various regions. The contribution by the meteorology to the surface PM2.5 concentrations from 2013 to 2019 was not found to show a consistent trend, fluctuating positively or negatively by about 5% on the annual average and 10–20% for the fall–winter heavy-pollution seasons.
Lei Zhang, Sunling Gong, Tianliang Zhao, Chunhong Zhou, Yuesi Wang, Jiawei Li, Dongsheng Ji, Jianjun He, Hongli Liu, Ke Gui, Xiaomei Guo, Jinhui Gao, Yunpeng Shan, Hong Wang, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 14, 703–718, https://doi.org/10.5194/gmd-14-703-2021, https://doi.org/10.5194/gmd-14-703-2021, 2021
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Development of chemical transport models with advanced physics and chemical schemes is important for improving air-quality forecasts. This study develops the chemical module CUACE by updating with a new particle dry deposition scheme and adding heterogenous chemical reactions and couples it with the WRF model. The coupled model (WRF/CUACE) was able to capture well the variations of PM2.5, O3, NO2, and secondary inorganic aerosols in eastern China.
Xiaoning Xie, Anmin Duan, Zhengguo Shi, Xinzhou Li, Hui Sun, Xiaodong Liu, Xugeng Cheng, Tianliang Zhao, Huizheng Che, and Yangang Liu
Atmos. Chem. Phys., 20, 11143–11159, https://doi.org/10.5194/acp-20-11143-2020, https://doi.org/10.5194/acp-20-11143-2020, 2020
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Observational and modeling results both show that the surface dust concentrations over the East Asian (EA) dust source region and over the northwestern Pacific (NP) in MAM are significantly positively correlated with TPSH. These atmospheric circulation anomalies induced by the increased TPSH result in increasing westerly winds over both EA and NP, which in turn increases dust emissions over the dust source and dust transport over these two regions, as well as the regional dust cycles.
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Short summary
Historical long-term PM2.5 records with high temporal resolution are essential but lacking for research and environmental management. Here, we reconstruct site-based and gridded PM2.5 datasets at 6-hour intervals from 1960 to 2020 that combine visibility, meteorological data, and emissions based on a machine learning model with extracted spatial features. These two PM2.5 datasets will lay the foundation of research studies associated with air pollution, climate change, and aerosol reanalysis.
Historical long-term PM2.5 records with high temporal resolution are essential but lacking for...
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